2D Code Reading with Local Inversion Compensation
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Solution Overview
Problem
2D codes marked on smooth plastic surfaces can suffer from optical inversion due to mirror-like reflectivity, leading to impaired code reading when only a sub-part of the code area is affected, which existing methods fail to address effectively.
Innovation Solution
The method involves receiving image data of a marked object, determining spatial correspondence with the 2D watermark indicia, identifying and adjusting image patches affected by inversion by correlating them with the reference signal component, and submitting the adjusted data for payload extraction, using techniques like median filtering and Pearson correlation to differentiate between normal and inverted patches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a 2D code is marked on a smooth plastic surface, then the code can be applied to the surface, but the mirror-like reflectivity causes optical inversion in sub-parts of the code, impairing code reading
Solution Approach 1:
The code area is divided into multiple sub-parts or patches, and each patch is independently analyzed for inversion. By segmenting the code into smaller regions, the system can identify and correct inversion in specific sub-parts without affecting the entire code, thus maintaining reliable code reading despite partial inversion caused by mirror-like reflectivity.
Solution Approach 2:
The invention applies local quality by treating different sub-parts of the code differently based on their inversion status. Each patch is individually assessed and corrected if inverted, rather than treating the entire code uniformly. This localized approach ensures that only affected regions are corrected, preserving overall code readability.
2Ease of operation
If existing methods are used to read 2D codes on plastic surfaces, then the reading process is simple, but they fail to address partial inversion effectively, leading to decoding failures
Solution Approach 1:
The invention performs preliminary action by detecting and correcting inversion in code sub-parts before the final decoding step. By proactively identifying inverted patches and adjusting them prior to payload extraction, the system prevents decoding failures that would otherwise occur due to partial inversion, ensuring complete information retrieval.
Solution Approach 2:
The system uses feedback by comparing the detected code pattern with the expected reference signal. When a sub-part shows inversion (indicated by negative correlation or unexpected pattern), the system feeds this information back to correct the inversion before final decoding, thereby preventing information loss and ensuring accurate payload extraction.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly improves the decoding performance of 2D codes by accurately identifying and compensating for inversion in sub-parts of the code, enhancing the robustness of watermark decoding on smooth plastic surfaces.
Implementation Method 1
The white cells reflect more incident illumination than do the black cells. A camera senses the spatial differences in reflected light
Implementation Method 2
When a code is marked on a smooth plastic surface, the mirror-like reflectivity of the surface can sometimes give rise to optical inversion in the appearance of the code—flipping relatively lighter and darker areas
Data Source
AI summary
In one aspect, the technology processes image data depicting a physical object to extract payload data that is encoded on the object in the form of tiled code blocks. The payload data is encoded in conjunction with an associated reference signal. To account for possible inversion of the imagery, the decoding includes determining spatial correspondence between the image data and the reference signal. A patch of the image data smaller than the block size is then selected, and correlated with a spatially-corresponding patch of the reference signal. From the correlation it may be concluded that the chosen patch exhibits inversion. In such case a subset of the image data is adjusted prior to decoding to compensate for the inversion. A great number of other features and arrangements are also detailed.